CN107179688A - Consider the Power System Reliability Analysis method of Monte Carlo state sampling truncation - Google Patents
Consider the Power System Reliability Analysis method of Monte Carlo state sampling truncation Download PDFInfo
- Publication number
- CN107179688A CN107179688A CN201710476314.2A CN201710476314A CN107179688A CN 107179688 A CN107179688 A CN 107179688A CN 201710476314 A CN201710476314 A CN 201710476314A CN 107179688 A CN107179688 A CN 107179688A
- Authority
- CN
- China
- Prior art keywords
- state
- sampling
- duration
- network
- msub
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention belongs to Power System Reliability Analysis technical field, more particularly to a kind of Power System Reliability Analysis method of consideration Monte Carlo state sampling truncation, step 1, input electric power system element data；Step 2, determine element original state；Step 3, adoption status duration sampling carry out Monte Carlo simulation, calculate no-failure operation duration and fault restoration duration；Step 4, to s states carry out Network topology, judge whether state s occurs network off-the-line into isolated island or system full cut-off；If it is the system sequence status switch truncation to obtaining；Step 6, one recovery state of a control of additional insertion, go to step 3；If otherwise judging whether to occur network off-the-line into isolated island or system full cut-off problem, if then setting up recovery Controlling model, otherwise judge that this network state whether there is cutting load problem, if any then setting up corrective control model；Resolving node cutting load amount and the total cutting load amount of system, the number of times of each system mode, duration, calculating obtain reliability index.
Description
Technical field
The invention belongs to Power System Reliability Analysis technical field, more particularly to a kind of consideration Monte Carlo state sampling
The Power System Reliability Analysis method of truncation.
Background technology
Power System Reliability is incessantly to electric power to power system by acceptable quality level (AQL) and requirement
User supplies the measurement of electric power and electric flux ability.Using the data such as system topology information and component reliability parameter, using solution
Analysis method (such as state space method etc.) or Monte Carlo simulation approach assess the every reliability index for system of studying.
In above-mentioned analysis method for reliability, analytic method is in processing system small scale, less demanding to complicated service condition
When it is more effective, handle complication system when, amount of calculation is in exponential increase with parts number；The great advantage of Monte Carlo Method is
Convergence rate is unrelated with the dimension of problem, more suitable for assessing extensive High Dimensional Systems, and/or the complicated power train of service condition
System integrity problem.
State duration sampling is usually used in Power System Reliability Analysis, and it is a kind of sequential (sequential) Monte Carlo
Simulation.This method is sampled based on the probability distribution to the element state duration, according to sequential at one span
(the need for studying a question, simulated time is up to hours up to a million) are simulated on degree, are carried out in each sample mode
Network analysis simultaneously calculates reliability index.
It can be seen from state duration sampling principle, sampling reaches self termination after the given time, belongs to timing
Truncated sampling data.The sample mode is disadvantageous in that：Do not consider each state when carrying out system mode analysis
Difference, acquiescence carries out Load flow calculation or cutting load to each state and calculates (belonging to Corrective control category), and then calculates and obtain
Reliability index.If in fact, the state that sampling is obtained is that off-the-line is (even whole into isolated island or most of generating set
Generating set) to stop transport, the obtained system shutdown duration of sampling sometimes is longer, and trend is only leaned in system mode analysis now
Calculate or cutting load calculating be inadequate, it is necessary to consider system full cut-off or grid-connected the necessarily involved control process of isolated island --- it is extensive
Multiple control calculates content.Present patent application content is perfect to existing sampling and the breakthrough of analysis method for reliability, and this is also this
The design original intention of patent of invention, is different from the difference of existing Model in Reliability Evaluation of Power Systems method.
The content of the invention
In order to grasp the reliability level of operation states of electric power system exactly, probe into Model in Reliability Evaluation of Power Systems
More meet the analysis method of system operation actual demand, the present invention proposes a kind of electricity of consideration Monte Carlo state sampling truncation
Force system analysis method for reliability, including：
Step 1, input for the power system component data needed for Load flow calculation and Calculation of Reliability, sample mode is set
Dynamic storage cell and final sample mode dynamic storage cell and total effective status counter, and initialize；
Step 2, the original state for determining each element in system, set all elements in initial time all in operation shape
State；
Step 3, adoption status duration sampling carry out Monte Carlo simulation, according to the fault rate of each element and reparation
Rate carries out state duration sampling to each element, calculates the no-failure operation duration for meeting exponential distribution and failure is repaiied
The multiple duration, if rudimentary system time sequence status number is N；
Step 4, s states are taken, if s>N then goes to step 8, otherwise carries out Network topology to s states, judges state
Whether s occurs network off-the-line into isolated island or system full cut-off；If it is 5 are gone to step, if it is not, then going to step 7；
Step 5, the system sequence status switch truncation obtained to wheel sampling, records the effective status that this wheel sampling is obtained
Number is s, adds end-state dynamic storage cell in order by this wheel state sampling results, s is added into total effective status
Counter, while emptying sample mode dynamic storage cell；
Step 6, additional one recovery state of a control of insertion in final sample mode dynamic storage cell, while total effective
State counter+1；3 are gone to step, next round sampling is re-started；
Step 7, accident analysis is carried out to the obtained each system mode of sampling, with judge whether to occur network off-the-line into
Isolated island or system full cut-off problem, if then going to step 8, otherwise go to step 9；
Step 8, foundation recover Controlling model, restore the system to original state：Most carried out soon for target with load restoration
Solve；
Step 9, judge that this network state whether there is cutting load problem, if any then setting up corrective control model：It is negative to cut
The minimum object function of lotus amount is solved；
Step 10, resolving node cutting load amount and the total cutting load amount of system, the number of times of each system mode, duration, meter
Calculation obtains reliability index.
The power system component data include network parameter and topology information, generated output power, sequential are negative
Lotus power data, element failure rate and repair rate, reliability simulation total time.
The computational methods of the no-failure operation duration and fault restoration duration are：
Wherein, U1、U2The uniform random number obtained when ∈ [0,1], respectively Monte-Carlo step.
The step 8 considers the constraint of power of the assembling unit limit value, ramping rate constraints, the constraint of busbar voltage limit value, using particle
Group's algorithm is solved.
The step 9 considers that the constraint of generated output power limit value, the constraint of Line Flow limit value, node voltage limit refer to constraint
Optimal Planning Model, and solved using particle cluster algorithm.
The beneficial effects of the present invention are：The analysis method for reliability of the present invention considers whole power system full cut-off or deposited
The recovery control process of isolated island after network off-the-line, recovers the power off time length that control strategy considers solution column region, reliably
Property indicator-specific statistics considers contribution of the different system state to index, closer to reality system occurrence scene；Analogy method is adopted
Monte Carlo simulation is carried out with state duration sampling, and is provided with for preserving sample mode dynamic storage cell and use
In the final sample mode dynamic storage cell of preservation；Sample mode is according to whether occur network off-the-line into isolated island or system full cut-off
And determine the sampled sequence whether truncation；Sample mode inserts a recovery state of a control due to truncation；Examined in state analysis
Consider the foundation and solution for recovering Controlling model；This method can more reasonably consider different sample modes especially system full cut-off or
The influence of network off-the-line state and its recovery control to reliability so that fail-safe analysis result can integrate meter and different system
State correction controls or recovered control program process and efficiency, and fail-safe analysis conclusion is more reasonable, effective.
Brief description of the drawings
Fig. 1 is Reliability Evaluation Algorithm flow chart of the invention.
Fig. 2 is initial system state sampling results schematic diagram.
Fig. 3 is the system mode sampling results schematic diagram after consideration truncation.
Embodiment
Below in conjunction with the accompanying drawings, embodiment is elaborated.
A kind of Power System Reliability Analysis method of consideration Monte Carlo state sampling truncation, as shown in Figure 1：
Step 1, input system component data, including network parameter and topology information, generated output power, sequential
Data needed for the Load flow calculations such as load power data；Component reliability data include element failure rate λ and repair rate μ, reliability
Simulate total time T etc.；It is provided for preserving the dynamic storage cell StateA () of sample mode information and initializes, sets and use
In the dynamic storage cell StateB () for preserving final sample mode information and initialize, total effective status counter is set
Ns_allJuxtaposition initial value is zero；
Step 2, the original state for determining each element in system, typically set all elements in initial time all in fortune
Row state；
Step 3, adoption status duration sampling carry out Monte Carlo simulation, for recoverable two state element
(generator, transmission line of electricity etc.), according to the fault rate λ and repair rate μ of each element, enters according to formula (1), (2) to each element
Row state duration is sampled, and calculates the no-failure operation duration τ for meeting exponential distribution1With fault restoration duration τ2,
It is additional in order to be stored in original state dynamic storage cell StateA (1)~StateA if rudimentary system time sequence status number is N
(N)；
Wherein, U1、U2The uniform random number obtained when ∈ [0,1], respectively Monte-Carlo step.
Step 4, s states are taken, if s>N then goes to step 8, otherwise carries out Network topology to s states；Judgement state
Whether s occurs network off-the-line into isolated island or system full cut-off；If it is 6 are gone to step, if otherwise going to step 7；
Step 5, the system sequence status switch truncation obtained to wheel sampling, records the effective status that this wheel sampling is obtained
Number is s, adds end-state dynamic storage cell StateB (N in order by this wheel state sampling resultss_all)~StateB
(Ns_all+ s), s is added to total effective status counter and realizes Ns_all=Ns_all+s；Empty StateA () simultaneously；
Step 6, in StateB (Ns_all) in additional one recovery state of a control of insertion, unison counter Ns_all=Ns_all+
1；3 are gone to step, next round sampling is re-started；
Step 7, accident analysis is carried out to the obtained each system mode of sampling, with judge whether to occur network off-the-line into
Isolated island or system full cut-off problem, if then going to step 8, otherwise go to step 9；
Step 8, foundation recover Controlling model, restore the system to original state：It is most fast for target with load restoration, it is considered to
Constrained to the constraint of power of the assembling unit limit value, ramping rate constraints, busbar voltage limit value etc., solved using particle cluster algorithm；
Step 9, judge that this network state whether there is cutting load problem, if any then needing to set up corrective control model：With
The minimum object function of cutting load amount, it is considered to the constraint of generated output power limit value, the constraint of Line Flow limit value, node voltage limit
Refer to the Optimal Planning Model of constraint etc., and solved using particle cluster algorithm；
Step 10, resolving node cutting load amount and the total cutting load amount of system, the number of times of each system mode, duration, meter
Calculation obtains reliability index.
Specific embodiment is as follows：
First, the Monte Carlo state sampling of truncation is considered
The state duration sampling for the consideration sample mode truncation that this patent is proposed, with three elements shown in Fig. 2 and Fig. 3
Illustrated exemplified by system.System is made up of the element of A, B, C tri-, and its internal annexation can be series, parallel or series-parallel connection knot
Structure.In sampling start time, all elements are typically set in initial time all in running status.Said in accordance with the following steps
It is bright：
1st, three component datas of input system, including network parameter and topology information, generated output power, sequential
Data needed for the Load flow calculations such as load power data；Component reliability data include element failure rate λ and repair rate μ, reliability
Simulate total time T etc.；It is provided for preserving the dynamic storage cell StateA () of sample mode information and initializes, sets and use
In the dynamic storage cell StateB () for preserving final sample mode information and initialize, total effective status counter is set
Ns_allJuxtaposition initial value is zero；
2nd, the original state of each element in system is determined, all elements are typically set in initial time all in operation shape
State；
3rd, adoption status duration sampling carries out Monte Carlo simulation, for each element, according to the event of each element
Barrier rate λ and repair rate μ, according to formulaIn formula, U1、U2∈ [0,1], respectively Meng Teka
The uniform random number that sieve is obtained when sampling, state duration sampling is carried out to each element, and calculating meets exponential distribution
No-failure operation duration τ1With fault restoration duration τ2, rudimentary system time sequence status number is N=11 herein, by suitable
Sequence adds deposit original state dynamic storage cell StateA (1)~StateA (11)；
4th, each state for taking sampling to obtain carries out Network topology, and system is transferred to if 11 states are all analyzed and finished
State computation link；When the 3rd state is s=3, system full cut-off is occurred in that, then obtained system sequence state of being sampled to the wheel
Sequence truncation, it is s=3 to record this and take turns obtained number of significant condition of sampling, and this wheel state sampling results is added most in order
Whole state dynamic storage cell StateB (1)~StateB (3), is added to total effective status counter by s=3 and realizes Ns_all
=Ns_all+3；Empty the later status informations of StateA (4) simultaneously；
5th, additional insertion one recovers state of a control StateB (4), unison counter N in StateB ()s_all=Ns_all
+1；
6th, next round sampling is re-started.
2nd, each sample mode carries out analysis calculating
1st, Load flow calculation
The network analysis of each sample mode is carried out using AC power flow method.In order to reduce amount of calculation, it can use and quickly open
Disconnected Load flow calculation strategy.
2nd, cutting load is calculated
When element, which is stopped transport, make it that system is unsatisfactory for operation constraint, the out-of-limit of system is eliminated by generation escheduling methods,
And avoid cutting load as far as possible.When cutting load is inevitable, cutting load amount should be caused minimum.Can now set up it is following it is optimal cut it is negative
Lotus model.
Object function：
Constraints：
PGimin≤PGi≤PGimax i∈NG
QGimin≤QGi≤QGimax i∈NG
0≤PCi≤PDi i∈NB
0≤QCi≤QDi i∈NB
Sij≤Sijmax(Sk)i,j∈NB
Wherein, PCiAnd QCiRespectively the active and load or burden without work reduction of node i, generally takes burden with power and idle negative
Lotus equal proportion is cut down, i.e. PCi/QCi=PDi/QDi；WiFor weight coefficient, reflect the importance degree of each node；PGiAnd QGiPoint
Not Wei the active and idle of generator node i exert oneself；PGimax、PGimin、QGimaxAnd QGiminRespectively generator node i is active
And idle bound of exerting oneself；PDiAnd QDiRespectively load bus i active and load or burden without work；SkRepresent to sample what is obtained k-th
System mode；SijFor the circuit apparent energy under system mode, SijmaxFor SkCircuit ij conveying power limits under state；NB is
Nodes, ND is load number, and NG is generator number.
3rd, recover control to calculate
Recover control process and be generally divided into three phases：Black starting-up stage, rack reconstruction stage, load restoration stage.It is extensive
The multiple power supply to load is through whole process.
(1), the black starting-up stage
The black starting-up stage is to provide startup from black starting-up power supply to thermal power plant neighbouring, with critical startup time restriction
Power, makes it recover generating capacity grid-connected process again.Black starting-up power supply, which is typically chosen, has self-startup ability in power network
Isolated subsystem after unit, such as conventional Hydropower Unit, pump-storage generator, gas turbine, the power support of external system, off-the-line
System etc..This stage recovery process includes：Black starting-up power initiation, charging relevant recovering path (including transformer, circuit etc.), open
Move the large-scale subsidiary engine in power plant, be activated the stable operation of set grid-connection, a certain amount of load of input to ensure system etc..Black starting-up
Stage will typically undergo 0.5~1h.
(2), rack reconstruction stage
The target of rack reconstruction stage is to recover main force's unit and trunk rack, relates generally to network structure, generator and opens
Dynamic order and restoration path optimization etc..Whole process will typically continue 3~4h.It can set up following using load restoration as target
Rack Restoration model, using particle swarm optimization algorithm.
First put into important load, the object function that secondary load is put on demand：
Constraint：
PGimin≤PGi≤PGimax i∈NG
QGimin≤QGi≤QGimax i∈NG
Vimin≤Vi≤Vimax i∈NB
PLi-TLiXi≤0i∈NL
In formula, L1And L2For important load and general load；xiAnd xj=0 or 1, represent whether load puts into；α and β difference
For weight coefficient, to represent that significance level is different；K and l are the number of load respectively；PLiAnd TLiThe active of circuit is represented respectively
Power and power threshold, Xi=0 or 1, represent whether the circuit is reconstructed process and chooses.
(3), the load restoration stage
The target in load restoration stage is as fast as possible, recovers load as much as possible.Now to consider system reality
Running status, verifies the constraint such as frequency, voltage, it is determined that the optimal load restoration amount of recovery operation every time；Then, it is negative according to node
The factors such as lotus place, size and its importance degree, determine load ordering in launching and overall recovery policy.This process will typically be held
Continue several hours.Load restoration Optimized model can be set up, using particle swarm optimization algorithm.
Still first put into important load, the object function that secondary load is put on demand：
Constraint：
fmin≤f(xili)
Pmin≤∑(xili)
Vset≤Vtr
Vimin≤Vi≤Vimax
0≤Sij≤Sijmax
In formula, f (xili) represent to recover system frequency minimum during load point；PminRepresent that the minimum of current grid-connected unit is steady
Make power；VtrRepresent transient voltage；Node voltage constraint and line power constraint during last 2 constraint representation stable states.
3rd, reliability index is calculated
Reliability index is calculated using classical reliability index, including：
(1) expect to lack power supply figureofmerit EENS (MWh/a)
Wherein, CiFor system mode i cutting load amount, MW；tiFor system mode i duration, T is sunykatuib analysis
Total time, S is the set of all cutting load states.
(2) cutting load probability level PLC
(3) cutting load number of times index EFLC (secondary/a)
Wherein, NiFor the system mode number of cutting load.
(4) average each cutting load duration ADLC (h/ times)
According to discussion above as can be seen that the power system of consideration Monte Carlo state sampling truncation proposed by the present invention
Analysis method for reliability is original creation.It can more reasonably consider different sample modes especially system full cut-off or network solution column-shaped
State and its recover influence of the control to reliability, make fail-safe analysis result of calculation more reasonable, be one it is effective and feasible, meet reality
The method on border.
Above-described embodiment is only the present invention preferably embodiment, but protection scope of the present invention is not limited to
This, any one skilled in the art the invention discloses technical scope in, the change that can readily occur in or replace
Change, should all be included within the scope of the present invention.Therefore, protection scope of the present invention should be with the protection model of claim
Enclose and be defined.
Claims (5)
1. a kind of Power System Reliability Analysis method of consideration Monte Carlo state sampling truncation, it is characterised in that including：
Step 1, input for the power system component data needed for Load flow calculation and Calculation of Reliability, sample mode dynamic is set
Memory cell and final sample mode dynamic storage cell and total effective status counter, and initialize；
Step 2, the original state for determining each element in system, set all elements in initial time all in running status；
Step 3, adoption status duration sampling carry out Monte Carlo simulation, according to the fault rate and repair rate pair of each element
Each element carries out state duration sampling, calculates the no-failure operation duration for meeting exponential distribution and fault restoration is held
The continuous time, if rudimentary system time sequence status number is N；
Step 4, s states are taken, if s>N then goes to step 8, otherwise carries out Network topology to s states, judges that state s is
It is no to occur network off-the-line into isolated island or system full cut-off；If it is 5 are gone to step, if it is not, then going to step 7；
Step 5, the wheel is sampled obtained system sequence status switch truncation, recording the number of significant condition that this wheel sampling obtains is
S, adds end-state dynamic storage cell in order by this wheel state sampling results, and s is added into total effective status counts
Device, while emptying sample mode dynamic storage cell；
Step 6, additional one recovery state of a control of insertion in final sample mode dynamic storage cell, while total effective status
Counter+1；3 are gone to step, next round sampling is re-started；
Step 7, each system mode progress accident analysis obtained to sampling, to judge whether to occur network off-the-line into isolated island
Or system full cut-off problem, if then going to step 8, otherwise go to step 9；
Step 8, foundation recover Controlling model, restore the system to original state：Most solved soon for target with load restoration；
Step 9, judge that this network state whether there is cutting load problem, if any then setting up corrective control model：With cutting load amount
Minimum object function is solved；
Step 10, resolving node cutting load amount and the total cutting load amount of system, the number of times of each system mode, duration, are calculated
To reliability index.
2. method according to claim 1, it is characterised in that the power system component data include network parameter and topology
Structural information, generated output power, sequential load power data, element failure rate and repair rate, reliability simulation total time.
3. method according to claim 1, it is characterised in that when the no-failure operation duration and fault restoration continue
Between computational methods be：
<mrow>
<msub>
<mi>&tau;</mi>
<mn>1</mn>
</msub>
<mo>=</mo>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mi>&lambda;</mi>
</mfrac>
<mi>ln</mi>
<msub>
<mi>U</mi>
<mn>1</mn>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>&tau;</mi>
<mn>2</mn>
</msub>
<mo>=</mo>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mi>&mu;</mi>
</mfrac>
<mi>ln</mi>
<msub>
<mi>U</mi>
<mn>2</mn>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, U1、U2The uniform random number obtained when ∈ [0,1], respectively Monte-Carlo step.
4. method according to claim 1, it is characterised in that the step 8 considers the constraint of power of the assembling unit limit value, creep speed
Constraint, the constraint of busbar voltage limit value, are solved using particle cluster algorithm.
5. method according to claim 1, it is characterised in that the step 9 considers the constraint of generated output power limit value, line
Road trend limit value constraint, node voltage limit refer to the Optimal Planning Model of constraint, and are solved using particle cluster algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710476314.2A CN107179688B (en) | 2017-06-21 | 2017-06-21 | Power system reliability analysis method considering Monte Carlo state sampling truncation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710476314.2A CN107179688B (en) | 2017-06-21 | 2017-06-21 | Power system reliability analysis method considering Monte Carlo state sampling truncation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107179688A true CN107179688A (en) | 2017-09-19 |
| CN107179688B CN107179688B (en) | 2020-06-26 |
Family
ID=59846170
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710476314.2A Expired - Fee Related CN107179688B (en) | 2017-06-21 | 2017-06-21 | Power system reliability analysis method considering Monte Carlo state sampling truncation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107179688B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107681691A (en) * | 2017-09-30 | 2018-02-09 | 太原理工大学 | The wind-electricity integration system operation reliability appraisal procedure of meter and uncertain factor |
| CN108122070A (en) * | 2017-12-12 | 2018-06-05 | 国家电网公司 | The definite method and apparatus of distribution network reliability |
| CN111353230A (en) * | 2020-03-03 | 2020-06-30 | 中国海洋石油集团有限公司 | Reliability assessment method and system for offshore oilfield power system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104123683A (en) * | 2014-08-01 | 2014-10-29 | 华中科技大学 | Electrical power system black-start scheme generation method based on dynamic programming |
| CN104218620A (en) * | 2014-09-26 | 2014-12-17 | 国家电网公司 | Active power distribution network reliability analysis method based on pseudo sequential Monte Carlo simulation |
| CN105160459A (en) * | 2015-08-15 | 2015-12-16 | 国家电网公司 | Evaluation method for stable operation state of power system |
| CN106058857A (en) * | 2016-06-30 | 2016-10-26 | 上海电力学院 | Active distribution network reliability evaluation method with consideration of load transferring and load shedding |
| CN106485392A (en) * | 2016-07-29 | 2017-03-08 | 国家电网公司 | Reliability of Interconnected Generating System appraisal procedure |
-
2017
- 2017-06-21 CN CN201710476314.2A patent/CN107179688B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104123683A (en) * | 2014-08-01 | 2014-10-29 | 华中科技大学 | Electrical power system black-start scheme generation method based on dynamic programming |
| CN104218620A (en) * | 2014-09-26 | 2014-12-17 | 国家电网公司 | Active power distribution network reliability analysis method based on pseudo sequential Monte Carlo simulation |
| CN105160459A (en) * | 2015-08-15 | 2015-12-16 | 国家电网公司 | Evaluation method for stable operation state of power system |
| CN106058857A (en) * | 2016-06-30 | 2016-10-26 | 上海电力学院 | Active distribution network reliability evaluation method with consideration of load transferring and load shedding |
| CN106485392A (en) * | 2016-07-29 | 2017-03-08 | 国家电网公司 | Reliability of Interconnected Generating System appraisal procedure |
Non-Patent Citations (2)
| Title |
|---|
| GARCIA ，ETC: "Time Step Truncation error in direct simulation Monte Carlo", 《PHYSICS OF FLUID》 * |
| 别朝虹等: "蒙特卡洛法在评估电力系统可靠性中的应用", 《电力系统自动化》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107681691A (en) * | 2017-09-30 | 2018-02-09 | 太原理工大学 | The wind-electricity integration system operation reliability appraisal procedure of meter and uncertain factor |
| CN107681691B (en) * | 2017-09-30 | 2020-01-10 | 太原理工大学 | Wind power grid-connected system operation reliability assessment method considering uncertainty factors |
| CN108122070A (en) * | 2017-12-12 | 2018-06-05 | 国家电网公司 | The definite method and apparatus of distribution network reliability |
| CN108122070B (en) * | 2017-12-12 | 2021-04-16 | 国家电网公司 | Method and device for determining reliability of power distribution network |
| CN111353230A (en) * | 2020-03-03 | 2020-06-30 | 中国海洋石油集团有限公司 | Reliability assessment method and system for offshore oilfield power system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107179688B (en) | 2020-06-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107785624B (en) | Method for evaluating performance of lithium battery | |
| CN112287504B (en) | Offline/online integrated simulation system and method for power distribution network | |
| CN112217202A (en) | Distributed new energy, energy storage and power distribution network planning method considering flexibility investment | |
| CN106992519B (en) | A kind of network load recovery robust Optimal methods based on information gap decision theory | |
| CN111209710B (en) | Automatic adjustment method and device for load flow calculation convergence | |
| CN107179688A (en) | Consider the Power System Reliability Analysis method of Monte Carlo state sampling truncation | |
| CN114006413B (en) | Power system transient stability control method and system based on graph neural network | |
| CN110676839A (en) | Method and system for evaluating reliability of alternating current-direct current hybrid power distribution network | |
| CN112909942B (en) | Active power distribution network fault recovery method and system based on hybrid solving strategy | |
| CN113077075B (en) | New energy uncertainty electric power system safety risk prevention control method and device | |
| CN111009914A (en) | Active power distribution network-oriented energy storage device location and volume determination method | |
| CN110148979A (en) | A kind of DC power supply battery group precisely control and dynamic optimization method online | |
| CN113468705A (en) | Power distribution system reliability analysis method containing microgrid based on path description | |
| CN110797918B (en) | Source network load system load recovery method and system based on closed-loop control | |
| CN111382518A (en) | Confidence capacity evaluation method of wind storage combined system | |
| CN113537646B (en) | Power grid equipment power failure maintenance scheme making method, system, equipment and storage medium | |
| CN116316607A (en) | Combined optimization scheduling method, system, equipment and medium | |
| CN115310142A (en) | Method, system, equipment and medium for verifying safety verification data of regional power grid | |
| CN112769124B (en) | Power system rapid operation risk assessment method based on tide transfer and tracking | |
| CN113591224A (en) | Urban power grid cascading failure risk assessment method and system | |
| Xu et al. | Risk assessment method for transmission-distribution integrated system with distributed PV | |
| CN111416345B (en) | Power distribution system reliability calculation method considering resource response randomness of demand side | |
| Shao et al. | A Method for Optimal Allocation of Distributed PV and Energy Storage Based on High Reliability of Distribution Network | |
| CN115603326B (en) | Power distribution network load transfer method and system based on tree topology | |
| CN110690719B (en) | Micro-grid battery energy storage configuration method and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200626 Termination date: 20210621 |